When an enterprise undertakes an Augmented Analytics project, it is typically doing so because it wishes to initiate data democratization, improve data literacy among its team members and create Citizen Data Scientists. The organization looks for a solution that is easy enough for its business users and intuitive enough to produce clear results; one that also provides sophisticated functionality and features and will produce a suitable Return on Investment (ROI) and Total Cost of Ownership (TCO).
Tag: Citizen Data Scientist
No matter the reason or the goal, when an enterprise chooses the right Augmented Analytics solution and carefully plans for and executes its implementation, it can optimize business results, reduce expenses and improve its market position, customer satisfaction and user adoption, and it is key to transforming business users to Citizen Data Scientists to improve results and team skills. Here, we examine the benefits of Augmented Analytics and how to plan and successfully execute an Augmented Analytics initiative.
How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics?
There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data. These enterprises will typically focus on building a team of data scientists or business analysts to help with this task OR they might take on an augmented analytics initiative to provide access to data and analytics for their business users. This is where businesses will often face a second issue; namely that the analytics solution they choose is not designed to easily and quickly provide insight into data and to ensure data quality.
Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Gartner recently estimated that the market for AI software will be nearly $134.8 billion, with the market growing by 31.1% in next several years. In a recent survey of C-suite executives, 80% of said they believe AI will transform their organizations, and 64% said it is the most transformational technology in a generation.
Use Key Influencer Analytics to Understand What Factors Impact Success!
Suppose you are trying to understand why a marketing campaign is failing, or what factors cause your customers to buy your services again. What if you need to know whether the color of a product affects the number of units sold in a particular country or area of a country? There are so many variables and components that can affect business success, and understanding the relationship among all the factors of success can seem like a guessing game.
When you are faced with this quandary, it is wise to use analytics to take the guesswork out of the equation. But how do you begin to analyze all the factors at play?
‘Can an augmented analytics tools tell you what algorithm or analytical technique to use? Can it help you understand the relationships of various factors and issues and determine what changes will help you improve the revenue for a product, or help you create a new service package?’
Statistical and analytical experts will tell you that there are three primary factors that can help you decide on the metrics to use for your analysis:
- The type of data you want to analyze – Understanding the data type can help you decide whether you need to consider a binary approach or look at categories, etc.
- The character of the data you want to analyze – Are you looking at product attributes, a specific threshold or data range, etc.
- What you want to accomplish with your analysis – Do you want to identify trends or patterns or are you trying to understand the relationships among the various factors and which factors affect success?
…and there is one more critical issue you must consider. Namely, who is doing the analysis? If you want to democratize data and improve data literacy across your enterprise, you will want your business users to understand and use analytical tools. But your team members are not statisticians or data scientists. So, they will need easy-to-use augmented analytics tools.
But can an augmented analytics tools tell you what algorithm or analytical technique to use? Can it help you understand the relationships of various factors and issues and determine what changes will help you improve the revenue for a product, or help you create a new service package?
One of the most frustrating tasks a business user has in analytics is finding and gathering the right data for analysis and ensuring that all factors, variables and data that may affect the outcome of the analysis is included. Depending on the size of the dataset a user selects, there may be hundreds or thousands of variables, and business users often find it difficult to identify the rights ones. Yet without the ability to identify the right variables, the business is likely to measure and attend to the wrong things.
That’s where Key Influencer Analytics comes into play! This approach puts the power and clarity of targeted analytics in the hands of business users and support Citizen Data Scientist initiatives and the critical goals of Data Literacy across the organization.
The user can simply point to the dataset they want to analyze and the system will identify the target and the influencers or predictors that will affect the target, along with its impact and it provides crucial metrics such as mean, outliers, and others and identifies relationship and distribution among variables. The system will auto-suggest relationships and present distribution and impact using the most appropriate visualization.
Users enjoy interactive features that allow them to see and explore other combinations and impacts and can select target and predictors, and use them for models, reports or KPIs. Key Influencer Analytics empowers every business user and allows them quickly select and target data to achieve results without the assistance of a data scientist, IT professional or analyst.
Key Influencer Analytics will:
- Identify feature importance based on machine learning algorithms
- Interpret insights in simple language
- Measure statistics
- Reveal influencers with impact on the target
- Auto recommend influencers
- Identify data relationships with interactive visualization
With these tools, business users can identify what matters most within the data, and how the various factors and relationships impact success, and they can understand the interdependence of variables and leverage auto-suggestions and machine learning functionality to gain insight. Users can also leverage the features within the tool to consider various combinations and the impact of those combinations on the success of the project, product or plan.
‘There are so many variables and components that can affect business success, and understanding the relationship among all the factors of success can seem like a guessing game.’
Find out how Key Influencer Analytics can benefit your business users and support Citizen Data Scientists, and how it can provide an advantage to your data scientists, business analysts and IT team members.
Original Post: Key Influencer Analytics Tells You How to Succeed!
Augmented Analytics with ALL Gartner Classified Essential Components AND Auto Insights Too!
Gartner classifies essential augmented analytical components and technologies as follows:
- Machine learning
- Natural Language generation and natural language processing
- Automation
‘Combine the three essential components specified by Gartner to create a comprehensive augmented analytics solution with refined auto insights and clear, concise results, the enterprise and its business users can perform complex data analytics and share analysis across the organization in a self-serve, mobile environment.’
While none of these is considered ‘new’ in the market today, the combination of essential components and the leveraging of new technologies and features is key to keeping augmented analytics fresh and usable for the average business user.
One such feature that users will look to for quick results and clear, concise decision support is the concept of auto insights.
Search Analytics is evolving at a rapid pace, and the concept of auto insights builds on the foundation of assisted predictive modeling and Clickless Analytics features, taking natural language processing (NLP) search analytics and predictive modeling to the next level.
Auto Insights frees business users and reduces the time and skills required to produce accurate, clear results, quickly and dependably, using machine learning that frees the business user to collect and analyze data with the guided assistance of a ‘smart’ solution.
Using this approach, business users will no longer have to select data columns or analysis techniques such as classification or clustering. Instead, the user will simply select the dataset to be analyzed. That’s it!. The system interprets the dataset, selects important columns of data, analyzes type and variety and other parameters and then uses intelligent machine learning to automatically apply the best algorithm and analytical technique and provide data insights.
Users can easily understand data and apply correlation, classification, regression, or forecasting, or other appropriate technique(s) based on the data the user wishes to analyze. Results are displayed using visualization types that provide the best fit for the data, and the interpretation is presented in simple natural language. This seamless, intuitive process enables business users to quickly and easily select and analyze data without guesswork or advanced skills.
By combining the three essential components specified by Gartner to create a comprehensive augmented analytics solution with refined insights and clear, concise results, the enterprise and its business users can perform complex data analytics and share analysis across the organization in a self-serve, mobile environment.
This approach provides support for sophisticated, advanced analytics and smart data visualization to automate the analysis process, so business users can simply point to a dataset and let the system do the rest.
‘The combination of essential components and the leveraging of new technologies and features is key to keeping augmented analytics fresh and usable for the average business user.’
Find out how Smarten Auto Insights can help your team and your organization. Leverage the essential components of Augmented Analytics, as defined by Gartner, and provide seamless, easy-to-use, sophisticated features and functionality to support your business users and transition your team members to Citizen Data Scientists.
3 Primary Components for Citizen Data Scientist Success!
The Citizen Data Scientist phenomenon is in full swing and, while the approach has its detractors, the proof is in success, and many organizations are actively succeeding using the Citizen Data Scientist approach.
Gartner has predicted that, in the future ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’
‘The enterprise does not expect to hire a legion of data scientists to perform analytics for every day-to-day need within the organization.’
There are many benefits of transitioning business users to a Citizen Data Scientist role, including:
- Improved data literacy
- Increased Data Democratization
- Improved Collaboration
- Increased Productivity
- Improved Alignment with Goals and Objectives
- Optimization of Data Scientist and IT Resources
- …and more!
There are many factors and components inherent in the success of a Citizen Data Scientist. If you are a Citizen Data Scientist candidate, there are three primary components of success:
- Organizational Commitment and Support – A business cannot just say they are committed to the Citizen Data Scientist approach. It must plan carefully and include a complete review of the current workflow, business processes and technology in order to make the changes required to support the new program. Citizen Data Scientists must be supported with revisions to performance evaluations and promotions. These revisions should encourage and enable the use of augmented analytics and collaboration so that business users are rewarded for acquiring and using new skills.
- Appropriate Augmented Analytics Tools – As with any other type of position or job, a Citizen Data Scientist needs the right tools to succeed. Without the right analytics tools, business users cannot make a successful transition to a Citizen Data Scientist role. The enterprise does not expect to hire a legion of data scientists to perform analytics for every day-to-day need within the organization but, if business users are expected to perform analytical activities, they will need easy-to-use tools that are sophisticated enough to achieve results, without requiring complex analytical skills or lengthy training. Augmented Analytics tools that are designed for business users should provide a foundation of machine learning and natural language processing (NLP) so search analytics is as easy as asking a question in a Google-type interface, with features like Smart Data Visualization, Assisted Predictive Modeling and Self-Serve Data Preparation.
- Curiosity and the Willingness to Explore – A prospective Citizen Data Scientist should have at least an average technology capability, with above average curiosity and a willingness to learn and collaborate. The ideal candidate should be recognized as someone who interacts well with others, and is willing to mentor others and help them become comfortable with new tools and processes.
‘If you are a Citizen Data Scientist candidate, there are three primary components of success.’
These are just a few of the factors you must consider when implementing a Citizen Data Scientist approach. Business users who are interested in becoming a Citizen Data Scientist must be willing to embrace new technology and tools and working at the leading edge of a new approach to collaboration and decision-making. initiative. Consider engaging an expert for your Citizen Data Scientist. IT consultants with experience and skill in this area can provide crucial support to help you succeed with your Citizen Data Scientist initiative and can provide simple Training Programs to bring your team on board and help them see the value to themselves and to the organization.
Original Post: Citizen Data Scientists Need These 3 Things to Succeed!
Is it the Right Time for My Business to Initiate a Citizen Data Scientist Program?
Whether you are a business owner, a business executive or a business manager, or you just like to keep up with industry trends, you no doubt have read about the transition of business users to Citizen Data Scientists. The topic has been in industry journals and publications for years, and it is still relevant today.
The Citizen Data Scientist Role and Benefits and How to Get Started!
Gartner has predicted that, ‘90% the world’s top 500 companies will have converged analytics governance into broader data and analytics governance initiatives.’ If you wish to compete, your business will want to follow the lead of these top companies. By incorporating analytics into day-to-day activities and allowing access for business users, the business can encourage the transition from business user to Citizen Data Scientist and create a comprehensive system of analytics with governance and collaboration to ensure security, appropriate access, mobile use and fact-based decision-making.
‘The path to Citizen Data Scientist does not have to be fraught with uncertainty.’
If you are a team member in a business environment, your role within that business is unique. But, it doesn’t matter what your role is or what your responsibilities are, it doesn’t matter what tasks or activities you must perform, you need good, solid data to make effective, fact-based decisions. And, your decisions must be swift because business is moving at the speed of light! Add to this the fact that your responsibilities seem to grow in scope and depth every day, and you can see how crucial it is to have the right support and solutions to help you achieve your goals.
If you are considering a transition to Citizen Data Scientist, or your business management team has informed you of their intent to make that transition, you probably have a lot of questions about the process and the role.
What is a Citizen Data Scientist?
Citizen Data Scientists are not business analysts or data scientists. They are business users and team members within the organization. The typical profile of an ideal Citizen Data Scientist is a person who is respected within the organization, and often shares data and information with other users to collaborate and produce outcomes that are designed to achieve goals and objectives and produce a successful outcome. These individuals may already be ‘power users’ of business applications and may have developed and reported or presented data to others with an eye toward clarifying their decision-making. Citizen Data Scientist candidates may also be IT team members who are interested in data science. In any case, these candidates will typically be uniquely curious, interested in data analytics and devoted to fact-based decisions and team collaboration. To fulfill the role of Citizen Data Scientists, business users will leverage augmented analytics solutions; that is analytics that provide simple recommendations and suggestions to help users easily choose visualization and predictive analytics techniques from within the analytical tool without the need for expert analytical skills.
Why is it important to implement a Citizen Data Scientist Program?
Business users can leverage domain and primary skills and expertise to gain insight into results using augmented analytics. Your team can interact with and collaborate with IT and data scientists to define business use cases and refine outcomes, and users are empowered to use analytical tools to hypothesize, prototype, forecast and predict, so the organization can identify issues, challenges and results prior to implementing changes in pricing, products, customer outreach, organizational structure, and all manner of operational decisions. The Citizen Data Scientist initiative will improve the accuracy of decisions, and allow users to share data and models with other users, thereby adding value to the organization. The enterprise can create and manage goals and objectives using measurable data and can improve time to market and make better decisions. The Citizen Data Scientist approach is crucial to sustained competitive advantage and to nurturing human resource assets, improving productivity and making the right decisions at the right time. Businesses and users can get ahead of the curve in the market and enable true collaboration.
‘The business can encourage the transition from business user to Citizen Data Scientist and create a comprehensive system of analytics.’
When a business makes the decision to implement a Citizen Data Scientist initiative, it must have a clear vision of the path to get business users to a new role, and to support those users with the augmented analytics tools and cultural changes required to encourage user adoption of analytics solutions and techniques. The path to Citizen Data Scientist does not have to be fraught with uncertainty. With the right cultural changes and management and technology support, the team can improve data literacy and encourage data democratization. Training is a key component of this success. But it needn’t be a lengthy, complex process.
An in-person or online training program that introduces the role and provides examples of Use Cases and Case Studies can help business users see how these tools might be used within the confines of their own role in the organization.
Explore the options her: One-Day In-Person Or Online Workshop For Citizen Data Scientists or Self-Paced eLearning Citizen Data Scientist sessions.
Find out more about the Citizen Data Scientist Path, Citizen Data Scientist Training and Augmented Analytics. We can help your business succeed and improve user adoption and data literacy.
Original Post : The What, Why and How of the Citizen Data Scientist Role!
How Should My Citizen Data Scientists Work with Data Scientists and Analysts?
Gartner has predicted that, ‘30% of organizations will harness the collective intelligence of their analytics communities, outperforming competitors that rely solely on centralized analytics or self-service.’ What does this mean for business Citizen Data Scientist initiatives? It does NOT mean that the organization should turn away from this type of initiative. What it DOES mean is that a business should recognize the value and purpose of each aspect of its analytical community.
